A New Approach to Quantify Functional Improvements Following X-Stop Spacer Procedure: A Case Report
Bibliographic record
Abstract
The purpose of this case report was to study effects of X-Stop implant surgery on the continuous spinal movement kinematics using 3D motion analysis. A proposed 3D kinematic spinal model was used to assess lumbar continuous active range of motion (AROM) during a standardized lumbar extension/flexion preoperatively and at 2 months postoperatively of a patient who underwent the X-Stop procedure. To investigate levels of muscle activities, electromyography recordings were made from right and left rectus abdominis, erector spinae and biceps femoris muscles. Also, functional mobility and patient quality of life were evaluated using the 6-minute walk test and the Swiss spinal stenosis and PROMIS-29 questionnaire. At 2 months postoperative, lumbar AROM increased by 18.5 and 14° for flexion and extension respectively and less muscle activation level was observed, despite the increase in lumbar AROM. Unlike positional magnetic resonance imaging (MRI) that is a common approach to assess spinal posture in static positions, the proposed continuous-motion analysis approach is able to analyze the lumbar AROM dynamically during flexion and extension. In this case report, the results indicate that the lumbar AROM, functional mobility and quality of life have been improved following X-Stop surgical intervention. J Med Cases. 2015;6(5):205-210 doi: https://doi.org/10.14740/jmc2104w
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".